Integration Test Mouse Thymus
knitr::opts_chunk$set(echo = TRUE)
library(devtools)## Loading required package: usethislibrary(rprojroot)
load_all()## ℹ Loading DSPWorkflow## Registered S3 method overwritten by 'GGally':
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knitr::opts_chunk$set(fig.width=12, fig.height=8, out.width = '100%')
knitr::opts_knit$set(root.dir = root)R Markdown
This runs the DSPworkflow package to completion using the Mouse Thymus Dataset:
1. Study Design:
# Set paths for downloading dcc files
downloads.path <- test_path("fixtures/Mouse_Thymus/downloaded/")
tar.file.name <- "thymus_dccs.tar.gz"
full.tar.path <- paste0(downloads.path,tar.file.name)
# Check if dcc files were previously downloaded
if (!file.exists(full.tar.path)) {
# Download dcc files and place in data folder
data.url <- "http://hpc.nih.gov/~CCBR/DSPWorkflow/thymus_dccs.tar.gz"
download.file(data.url, full.tar.path)
untar(full.tar.path, exdir = downloads.path)
}
dcc.files <- dir(
file.path(
downloads.path,
"dccs"
),
pattern = ".dcc$",
full.names = TRUE,
recursive = TRUE
)
pkc.files <-
test_path("fixtures/Mouse_Thymus/Mm_R_NGS_WTA_v1.0.pkc")
pheno.data.file <-
test_path("fixtures/Mouse_Thymus/Thymus_Annotation_updated_3.xlsx")
sdesign.list <- studyDesign(dcc.files = dcc.files,
pkc.files = pkc.files,
pheno.data.file = pheno.data.file,
pheno.data.sheet = "Annotation",
pheno.data.dcc.col.name = "Sample_ID",
protocol.data.col.names = c("aoi", "roi"),
experiment.data.col.names = c("panel"),
slide.name.col = "slide name",
class.col = "class",
region.col = "region",
segment.col = "segment",
area.col = "area",
nuclei.col = "nuclei")
# For creating fixture RDS
create.rds <- TRUE
if(create.rds) {
study.design.mouse.thymus <- sdesign.list$object
saveRDS(study.design.mouse.thymus, file = "tests/testthat/fixtures/Mouse_Thymus/studyDesignMouseThymus.RDS")
}
print(sdesign.list$sankey.plot)
print("Created GeoMx Object\n\n")## [1] "Created GeoMx Object\n\n" pData(sdesign.list$object)[,c("slide_name","class","segment")] ## slide_name class segment
## DSP-1001660007393-A-A02.dcc 1057/1059 Thymus B220
## DSP-1001660007393-A-A03.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-A04.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-A05.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-A06.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-A07.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-A08.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-A09.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-A10.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-A11.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-A12.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-B01.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-B02.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-B03.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-B04.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-B05.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-B06.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-B07.dcc 1057/1059 Thymus B220
## DSP-1001660007393-A-B08.dcc 1057/1059 Thymus B220
## DSP-1001660007393-A-B09.dcc 1057/1059 Thymus B220
## DSP-1001660007393-A-B10.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-B11.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-B12.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-C01.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-C02.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-C03.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-C04.dcc 1057/1059 Thymus PanCK
## DSP-1001660007393-A-C05.dcc 1057/1059 Thymus CD3
## DSP-1001660007393-A-C06.dcc 1072/1043 Thymus B220
## DSP-1001660007393-A-C07.dcc 1072/1043 Thymus B220
## DSP-1001660007393-A-C08.dcc 1072/1043 Thymus B220
## DSP-1001660007393-A-C09.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-C10.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-C11.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-C12.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-D01.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-D02.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-D03.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-D04.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-D05.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-D06.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-D07.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-D08.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-D09.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-D10.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-D11.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-D12.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-E01.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-E02.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-E03.dcc 1072/1043 Thymus B220
## DSP-1001660007393-A-E04.dcc 1072/1043 Thymus B220
## DSP-1001660007393-A-E05.dcc 1072/1043 Thymus B220
## DSP-1001660007393-A-E06.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-E07.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-E08.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-E09.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-E10.dcc 1072/1043 Thymus CD3
## DSP-1001660007393-A-E11.dcc 1072/1043 Thymus PanCK
## DSP-1001660007393-A-E12.dcc 1042/1044 Thymoma PanCK
## DSP-1001660007393-A-F01.dcc 1042/1044 Thymoma CD3
## DSP-1001660007393-A-F02.dcc 1042/1044 Thymoma PanCK
## DSP-1001660007393-A-F03.dcc 1042/1044 Thymoma CD3
## DSP-1001660007393-A-F04.dcc 1042/1044 Thymoma PanCK
## DSP-1001660007393-A-F05.dcc 1042/1044 Thymoma CD3
## DSP-1001660007393-A-F06.dcc 1042/1044 Thymoma PanCK
## DSP-1001660007393-A-F07.dcc 1042/1044 Thymoma B220
## DSP-1001660007393-A-F08.dcc 1042/1044 Thymoma B220
## DSP-1001660007393-A-F09.dcc 1042/1044 Thymoma B220
## DSP-1001660007393-A-F10.dcc 1042/1044 Thymoma B220
## DSP-1001660007393-A-F11.dcc 1042/1044 Thymoma B220
## DSP-1001660007393-A-F12.dcc 1042/1044 Thymoma B220
## DSP-1001660007393-A-G01.dcc 1042/1044 Thymoma CD3
## DSP-1001660007393-A-G02.dcc 1042/1044 Thymoma PanCK
## DSP-1001660007393-A-G03.dcc 1042/1044 Thymoma CD3
## DSP-1001660007393-A-G04.dcc 1042/1044 Thymoma PanCK
## DSP-1001660007393-A-G05.dcc 1042/1044 Thymoma CD3
## DSP-1001660007393-A-G06.dcc 1042/1044 Thymoma PanCK
## DSP-1001660007393-A-G07.dcc 1082/1157 Thymoma B220
## DSP-1001660007393-A-G08.dcc 1082/1157 Thymoma B220
## DSP-1001660007393-A-G09.dcc 1082/1157 Thymoma B220
## DSP-1001660007393-A-G10.dcc 1082/1157 Thymoma PanCK
## DSP-1001660007393-A-G11.dcc 1082/1157 Thymoma CD3
## DSP-1001660007393-A-G12.dcc 1082/1157 Thymoma PanCK
## DSP-1001660007393-A-H01.dcc 1082/1157 Thymoma CD3
## DSP-1001660007393-A-H02.dcc 1082/1157 Thymoma PanCK
## DSP-1001660007393-A-H03.dcc 1082/1157 Thymoma CD3
## DSP-1001660007393-A-H04.dcc 1082/1157 Thymoma B220
## DSP-1001660007393-A-H05.dcc 1082/1157 Thymoma B220
## DSP-1001660007393-A-H06.dcc 1082/1157 Thymoma PanCK
## DSP-1001660007393-A-H07.dcc 1082/1157 Thymoma CD3
## DSP-1001660007393-A-H08.dcc 1082/1157 Thymoma PanCK
## DSP-1001660007393-A-H09.dcc 1082/1157 Thymoma CD3
## DSP-1001660007393-A-H10.dcc 1082/1157 Thymoma PanCK
## DSP-1001660007393-A-H11.dcc 1082/1157 Thymoma CD3
## DSP-1001660007393-A-H12.dcc 1082/1157 Thymoma B2202. QC Preprocessing:
qc.output <- qcProc(object = sdesign.list$object,
min.segment.reads = 1000,
percent.trimmed = 80,
percent.stitched = 80,
percent.aligned = 80,
percent.saturation = 50,
min.negative.count = 1,
max.ntc.count = 1000,
min.nuclei = 200,
min.area = 16000,
print.plots = TRUE)
##
##
## Table: Summary for the NTC values
##
## |NTC Count | # of Segments|
## |:---------|-------------:|
## |146 | 95|
##
##
## Table: QC Summary for each Segment
##
## | | Pass| Warning|
## |:-------------|----:|-------:|
## |LowReads | 95| 0|
## |LowTrimmed | 95| 0|
## |LowStitched | 95| 0|
## |LowAligned | 95| 0|
## |LowSaturation | 95| 0|
## |LowNegatives | 95| 0|
## |HighNTC | 95| 0|
## |LowNuclei | 92| 3|
## |LowArea | 90| 5|
## |TOTAL FLAGS | 90| 5|
##
##
## Table: Summary for Segment QC Removal
##
## | | # Before Removal| # After Removal|
## |:--------|----------------:|---------------:|
## |Features | 20175| 20175|
## |Samples | 95| 90|
##
##
## Table: Summary for Probe QC Calls (Grubb's Outlier Test)
##
## | Passed| Global| Local|
## |------:|------:|-----:|
## | 20167| 0| 8|
##
##
## Table: Summary for Probe QC Removal
##
## | | # Before Collapsing| # After Collapsing|
## |:--------|-------------------:|------------------:|
## |Features | 20175| 20175|
## |Samples | 90| 90|
##
##
## Table: Summary for Gene-level Counts
##
## | | # Before Collapsing| # After Collapsing|
## |:--------|-------------------:|------------------:|
## |Features | 20175| 19963|
## |Samples | 90| 90| print(qc.output$segments.qc)## NULL create.rds <- TRUE
if(create.rds) {
qc.mouse.thymus <- qc.output$object
saveRDS(qc.mouse.thymus, file = "tests/testthat/fixtures/Mouse_Thymus/qcMouseThymus.RDS")
}3. Filtering:
goi <- c("Plb1", "Ccr7", "Oas2", "Oas1a", "Oas1b", "Rhbdl2", "Dlst", "Naa15", "Rab11a", "Desi1", "Tfdp1", "Foxn1")
filtering.output <- filtering(object = qc.output$object,
loq.cutoff = 2,
loq.min = 2,
cut.segment = .05,
goi = goi)
print(filtering.output$`stacked.bar.plot`)
print(filtering.output$`tab`)##
##
## | | Thymoma| Thymus|
## |:------|-------:|------:|
## |<1% | 0| 0|
## |1-5% | 0| 0|
## |5-10% | 0| 3|
## |10-15% | 0| 8|
## |>15% | 37| 42| print(filtering.output$`sankey.plot`)
print(filtering.output$`genes.detected.plot`)
create.rds <- TRUE
if(create.rds) {
filtering.mouse.thymus <- filtering.output$object
saveRDS(filtering.mouse.thymus, file = "tests/testthat/fixtures/Mouse_Thymus/filteringMouseThymus.RDS")
}4. Normalization:
q3.normalization.output <- geomxNorm(
object = filtering.output$object,
norm = "q3")## Using Segment, Annotation as id variables## No id variables; using all as measure variables
## No id variables; using all as measure variables print(q3.normalization.output$multi.plot)
print(q3.normalization.output$boxplot.raw)
print(q3.normalization.output$boxplot.norm)
neg.normalization.output <- geomxNorm(
object = filtering.output$object,
norm = "neg")## Using Segment, Annotation as id variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables print(neg.normalization.output$multi.plot)
print(neg.normalization.output$boxplot.raw)
print(neg.normalization.output$boxplot.norm)
create.rds <- TRUE
if(create.rds) {
q3.normalization.mouse.thymus <- q3.normalization.output$object
saveRDS(q3.normalization.mouse.thymus, file = "tests/testthat/fixtures/Mouse_Thymus/q3normalizationMouseThymus.RDS")
neg.normalization.mouse.thymus <- neg.normalization.output$object
saveRDS(neg.normalization.mouse.thymus, file = "tests/testthat/fixtures/Mouse_Thymus/negnormalizationMouseThymus.RDS")
}5. Unsupervised Analysis:
#Test Unsupervised Analysis:
unsupervised.output <- dimReduct(object = q3.normalization.output$object,
point.size = 3,
point.alpha = 1,
color.variable1 = "region",
shape.variable = "class"
)## using q_norm in the dimensional reductions## adding in the phenoData PCA, tSNE, and UMAP coordinates print(unsupervised.output$plot$PCA)
print(unsupervised.output$plot$tSNE)
print(unsupervised.output$plot$UMAP)
6. Clustering high CV Genes and Heatmap:
heatmap.output <- heatMap(object = unsupervised.output$object,
ngenes = 200,
scale.by.row.or.col = "row",
show.rownames = FALSE,
show.colnames = FALSE,
clustering.method = "average",
cluster.rows = TRUE,
cluster.cols = TRUE,
clustering.distance.rows = "correlation",
clustering.distance.cols = "correlation",
annotation.row = NA,
annotation.col = c("class", "segment", "region"),
breaks.by.values = seq(-3, 3, 0.05),
heatmap.color = colorRampPalette(c("blue", "white", "red"))(120),
norm.method = "quant")## Warning: It not suggested to both set `scale` and `breaks`. It makes the
## function confused. print(heatmap.output$plot)
7. Differential Expression Analysis:
goi <- c("Plb1", "Ccr7", "Oas2", "Oas1a", "Oas1b", "Rhbdl2", "Dlst",
"Naa15", "Rab11a", "Desi1", "Tfdp1", "Foxn1")
object <- q3.normalization.output$object
object <- object[goi,]
Gene <- Subset <- NULL
#First analysis:
reslist.1 <- diffExpr(object = object,
analysis.type = "Within Groups",
region.col = "region",
regions = c("Cortical", "Medullar"),
group.col = "class",
groups = c("Thymus"),
n.cores = 4)## At least one of the regions within the Region Column was not selected
## and is excluded: Unspecified, Tumor## Running Within Group Analysis between Regions
## Number of regions in group Thymus: 2 grid.draw(reslist.1$sample_table)
grid.newpage()
grid.draw(reslist.1$summary_table)
lfc_col1 <- colnames(reslist.1$result)[grepl("logFC",colnames(reslist.1$result))]
pval_col1 <- colnames(reslist.1$result)[grepl("_pval",colnames(reslist.1$result))]
lfc.1 <- reslist.1$result %>%
dplyr::filter(Gene == "Ccr7" & Subset == "Thymus") %>%
select(all_of(lfc_col1)) %>%
as.numeric()
pval.1 <- reslist.1$result %>%
dplyr::filter(Gene == "Ccr7" & Subset == "Thymus") %>%
select(all_of(pval_col1)) %>%
as.numeric()
cat(paste0("\n\nvalue of Ccr7 Fold Change is:", lfc.1))##
##
## value of Ccr7 Fold Change is:-1.5874 cat("expected value is -1.645")## expected value is -1.645 cat(paste0("\nvalue of Ccr7 pval is:",pval.1))##
## value of Ccr7 pval is:7.39e-06 cat("expected value is 0.0274")## expected value is 0.0274 #Second analysis:
reslist.2 <- diffExpr(object = object,
analysis.type = "Between Groups",
region.col = "segment",
regions = c("PanCK"),
group.col = "region",
groups = c("Tumor", "Medullar"),
n.cores = 1)## At least one of the regions within the Region Column was not selected
## and is excluded: CD3, B220## Running Between Group Analysis for Regions
## Number of groups in region PanCK: 2 grid.draw(reslist.2$sample_table)
grid.newpage()
grid.draw(reslist.2$summary_table)
lfc_col2 <- colnames(reslist.2$result)[grepl("logFC",colnames(reslist.2$result))]
pval_col2 <- colnames(reslist.2$result)[grepl("_pval",colnames(reslist.2$result))]
lfc.2 <- reslist.2$result %>%
dplyr::filter(Gene == "Ccr7" & Subset == "PanCK") %>%
select(all_of(lfc_col2)) %>%
as.numeric()
pval.2 <- reslist.2$result %>%
dplyr::filter(Gene == "Ccr7" & Subset == "PanCK") %>%
select(all_of(pval_col2)) %>%
as.numeric()
cat(paste0("\n\nvalue of Ccr7 Fold Change is: ", lfc.2))##
##
## value of Ccr7 Fold Change is: -1.64 cat("expected value is -1.89")## expected value is -1.89 cat(paste0("\nvalue of Ccr7 pval is: ",pval.2))##
## value of Ccr7 pval is: 7.11e-09 cat("expected value is 4.97e-12")## expected value is 4.97e-128. Volcano Plot
#This part is run on NIDAP.
9. Violin Plot
genes <- c("Plb1", "Ccr7", "Oas2", "Oas1a", "Oas1b", "Rhbdl2", "Dlst",
"Naa15", "Rab11a", "Desi1", "Tfdp1", "Foxn1")
violin.plot.test <- violinPlot(object = q3.normalization.output$object,
expr.type = "q_norm",
genes = genes,
group = "region",
facet.by = "segment")## [1] " not found and will not be displayed" grid.arrange(violin.plot.test)
10. Spatial Deconvolution:
ref.mtx = read.csv(test_path("fixtures", "sample_spatial_deconv_mtx.csv"),
row.names=1, check.names=FALSE)
rownames(ref.mtx) = sample(rownames(q3.normalization.output$object), size = 1500, replace = FALSE)
ref.annot = read.csv(test_path("fixtures", "ref_annot.csv"))
spatial.output <- spatialDeconvolution(object = q3.normalization.output$object,
expr.type = "q_norm",
ref.mtx = ref.mtx,
ref.annot = ref.annot,
prof.mtx = NULL,
use.custom.prof.mtx = TRUE,
cell.id.col = "CellID",
celltype.col = "LabeledCellType",
group.by = "segment")## Warning in create_profile_matrix(mtx = ref.mtx, cellAnnots = ref.annot, : not
## all cellNameCol names are in count matrix; 5311 cells are missing## [1] "Creating Atlas"
## [1] "1 / 21 : cTEC"
## [1] "2 / 21 : cTEC(cycling)"
## [1] "3 / 21 : mTEC_I"
## [1] "4 / 21 : mTEC_II"
## [1] "5 / 21 : TEC_Cldn10"
## [1] "6 / 21 : Epi(lung)"
## [1] "7 / 21 : muscle"
## [1] "8 / 21 : Fb_Postn"
## [1] "9 / 21 : mTEC_III"
## [1] "10 / 21 : Immune"
## [1] "11 / 21 : TEC(neuro)_like_1"
## [1] "12 / 21 : mTEC_IV(tuft)"
## [1] "13 / 21 : Fb_Aldh1a2"
## [1] "14 / 21 : Fb_Pi16"
## [1] "15 / 21 : Endo"
## [1] "16 / 21 : Mac"
## [1] "17 / 21 : TEC(neuro)_like_2"
## [1] "18 / 21 : Epi_Gcm2"
## [1] "19 / 21 : VSMC"
## [1] "20 / 21 : Epi_PAX8"
## [1] "21 / 21 : Ery"## Using celltype as id variables## Warning in xtfrm.data.frame(x): cannot xtfrm data frames print(spatial.output$figures)## $abundance.heatmap
##
## $cell.profile.heatmap
##
## $composition.barplot
print("Spatial Deconvolution Done")## [1] "Spatial Deconvolution Done"